/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    M5P.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees;

import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.trees.m5.M5Base;
import weka.classifiers.trees.m5.Rule;
import weka.core.Drawable;
import weka.core.Option;
import weka.core.Utils;

/**
 * <!-- globalinfo-start --> M5Base. Implements base routines for generating M5
 * Model trees and rules<br/>
 * The original algorithm M5 was invented by R. Quinlan and Yong Wang made
 * improvements.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint
 * Conference on Artificial Intelligence, Singapore, 343-348, 1992.<br/>
 * <br/>
 * Y. Wang, I. H. Witten: Induction of model trees for predicting continuous
 * classes. In: Poster papers of the 9th European Conference on Machine
 * Learning, 1997.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;inproceedings{Quinlan1992,
 *    address = {Singapore},
 *    author = {Ross J. Quinlan},
 *    booktitle = {5th Australian Joint Conference on Artificial Intelligence},
 *    pages = {343-348},
 *    publisher = {World Scientific},
 *    title = {Learning with Continuous Classes},
 *    year = {1992}
 * }
 * 
 * &#64;inproceedings{Wang1997,
 *    author = {Y. Wang and I. H. Witten},
 *    booktitle = {Poster papers of the 9th European Conference on Machine Learning},
 *    publisher = {Springer},
 *    title = {Induction of model trees for predicting continuous classes},
 *    year = {1997}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -N
 *  Use unpruned tree/rules
 * </pre>
 * 
 * <pre>
 * -U
 *  Use unsmoothed predictions
 * </pre>
 * 
 * <pre>
 * -R
 *  Build regression tree/rule rather than a model tree/rule
 * </pre>
 * 
 * <pre>
 * -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf
 *  (default 4)
 * </pre>
 * 
 * <pre>
 * -L
 *  Save instances at the nodes in
 *  the tree (for visualization purposes)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
 * @version $Revision$
 */
public class M5P extends M5Base implements Drawable {

    /** for serialization */
    static final long serialVersionUID = -6118439039768244417L;

    /**
     * returns information about the classifier
     *
     * @return a description suitable for displaying in the explorer/experimenter
     *         gui
     */
    public String globalInfo() {
        return "Implements the M5' model tree algorithm. " + "The original M5 algorithm was developed by R. Quinlan. Yong Wang " + "made improvements leading to M5'.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString();
    }

    /**
     * Creates a new <code>M5P</code> instance.
     */
    public M5P() {
        super();
        setGenerateRules(false);
    }

    /**
     * Returns the type of graph this classifier represents.
     * 
     * @return Drawable.TREE
     */
    @Override
    public int graphType() {
        return Drawable.TREE;
    }

    /**
     * Return a dot style String describing the tree.
     * 
     * @return a <code>String</code> value
     * @throws Exception if an error occurs
     */
    @Override
    public String graph() throws Exception {
        StringBuffer text = new StringBuffer();

        text.append("digraph M5Tree {\n");
        Rule temp = m_ruleSet.get(0);
        temp.topOfTree().graph(text);
        text.append("}\n");
        return text.toString();
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String saveInstancesTipText() {
        return "Whether to save instance data at each node in the tree for " + "visualization purposes.";
    }

    /**
     * Set whether to save instance data at each node in the tree for visualization
     * purposes
     * 
     * @param save a <code>boolean</code> value
     */
    public void setSaveInstances(boolean save) {
        m_saveInstances = save;
    }

    /**
     * Get whether instance data is being save.
     * 
     * @return a <code>boolean</code> value
     */
    public boolean getSaveInstances() {
        return m_saveInstances;
    }

    /**
     * Returns an enumeration describing the available options
     * 
     * @return an enumeration of all the available options
     */
    @Override
    public Enumeration<Option> listOptions() {
        Enumeration<Option> superOpts = super.listOptions();

        Vector<Option> newVector = new Vector<Option>();
        while (superOpts.hasMoreElements()) {
            newVector.addElement(superOpts.nextElement());
        }

        newVector.addElement(new Option("\tSave instances at the nodes in\n" + "\tthe tree (for visualization purposes)", "L", 0, "-L"));
        return newVector.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -N
     *  Use unpruned tree/rules
     * </pre>
     * 
     * <pre>
     * -U
     *  Use unsmoothed predictions
     * </pre>
     * 
     * <pre>
     * -R
     *  Build regression tree/rule rather than a model tree/rule
     * </pre>
     * 
     * <pre>
     * -M &lt;minimum number of instances&gt;
     *  Set minimum number of instances per leaf
     *  (default 4)
     * </pre>
     * 
     * <pre>
     * -L
     *  Save instances at the nodes in
     *  the tree (for visualization purposes)
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {
        setSaveInstances(Utils.getFlag('L', options));
        super.setOptions(options);
    }

    /**
     * Gets the current settings of the classifier.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    @Override
    public String[] getOptions() {
        String[] superOpts = super.getOptions();
        String[] options = new String[superOpts.length + 1];
        int current = superOpts.length;
        for (int i = 0; i < current; i++) {
            options[i] = superOpts[i];
        }

        if (getSaveInstances()) {
            options[current++] = "-L";
        }

        while (current < options.length) {
            options[current++] = "";
        }

        return options;
    }

    /**
     * Main method by which this class can be tested
     * 
     * @param args an array of options
     */
    public static void main(String[] args) {
        runClassifier(new M5P(), args);
    }
}
